A Comparison of the Chicken and Turkey Proteomes and Phosphoproteomes in the Development of Poultry-Specific Immuno-Metabolism Kinome Peptide Arrays
Bibliographic record
Abstract
The use of species-specific peptide arrays for the study of animal kinomes has a proven track record of success. This technique has been used in a variety of species for the study of host-pathogen interactions and metabolism. Species-specific peptide arrays have been designed previously for use with chicken but a turkey array has never been attempted. In addition, arrays designed around individual cellular functions have been designed and utilized, but cross-function immuno-metabolic arrays have not been considered previously. Antecedent to designing separate chicken and turkey immuno-metabolic kinome peptide arrays, we show that while the chicken and turkey genomes are quite similar, the two species are much more distinct at the proteome and phosphoproteome levels. Despite a genome identity of approximately 90%, we observe that only 83% of chicken and turkey orthologous proteins display sequence matches between the two species. Further, less than 70% of kinase recognition target sequences are exact matches between chicken and turkey. Thus, our analysis shows that, at the proteome and kinome level, these two species must be considered separately in the design of novel peptide arrays. Our ultimate array design covers numerous immune and metabolic processes including innate and adaptive immunity, inflammatory responses, carbohydrate, protein, and fat metabolism, and response to hormones. We have shown the proteomic and phosphoproteomic diversity of chicken and turkey and have designed a valuable research tool for the study of immuno-metabolism within these two species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".